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Articles 1 - 5 of 5
Full-Text Articles in Biostatistics
Sample Size Calculation Of Clinical Trials With Correlated Outcomes, Dateng Li
Sample Size Calculation Of Clinical Trials With Correlated Outcomes, Dateng Li
Statistical Science Theses and Dissertations
In this thesis, we investigate sample size calculation for three kinds of clinical trials: (1). Randomized controlled trials (RCTs) with longitudinal count outcomes; (2). Cluster randomized trials (CRTs) with count outcomes; (3). CRTs with multiple binary co-primary endpoints.
Copula-Based Zero-Inflated Count Time Series Models, Mohammed Sulaiman Alqawba
Copula-Based Zero-Inflated Count Time Series Models, Mohammed Sulaiman Alqawba
Mathematics & Statistics Theses & Dissertations
Count time series data are observed in several applied disciplines such as in environmental science, biostatistics, economics, public health, and finance. In some cases, a specific count, say zero, may occur more often than usual. Additionally, serial dependence might be found among these counts if they are recorded over time. Overlooking the frequent occurrence of zeros and the serial dependence could lead to false inference. In this dissertation, we propose two classes of copula-based time series models for zero-inflated counts with the presence of covariates. Zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), and zero-inflated Conway-Maxwell-Poisson (ZICMP) distributed marginals of the …
Feature Selection For Longitudinal Data By Using Sign Averages To Summarize Gene Expression Values Over Time, Suyan Tian, Chi Wang
Feature Selection For Longitudinal Data By Using Sign Averages To Summarize Gene Expression Values Over Time, Suyan Tian, Chi Wang
Biostatistics Faculty Publications
With the rapid evolution of high-throughput technologies, time series/longitudinal high-throughput experiments have become possible and affordable. However, the development of statistical methods dealing with gene expression profiles across time points has not kept up with the explosion of such data. The feature selection process is of critical importance for longitudinal microarray data. In this study, we proposed aggregating a gene’s expression values across time into a single value using the sign average method, thereby degrading a longitudinal feature selection process into a classic one. Regularized logistic regression models with pseudogenes (i.e., the sign average of genes across time as predictors) …
Bayesian Nonparametric Analysis Of Longitudinal Data With Non-Ignorable Non-Monotone Missingness, Yu Cao
Bayesian Nonparametric Analysis Of Longitudinal Data With Non-Ignorable Non-Monotone Missingness, Yu Cao
Theses and Dissertations
In longitudinal studies, outcomes are measured repeatedly over time, but in reality clinical studies are full of missing data points of monotone and non-monotone nature. Often this missingness is related to the unobserved data so that it is non-ignorable. In such context, pattern-mixture model (PMM) is one popular tool to analyze the joint distribution of outcome and missingness patterns. Then the unobserved outcomes are imputed using the distribution of observed outcomes, conditioned on missing patterns. However, the existing methods suffer from model identification issues if data is sparse in specific missing patterns, which is very likely to happen with a …
Variable Selection In Accelerated Failure Time (Aft) Frailty Models: An Application Of Penalized Quasi-Likelihood, Sarbesh R. Pandeya
Variable Selection In Accelerated Failure Time (Aft) Frailty Models: An Application Of Penalized Quasi-Likelihood, Sarbesh R. Pandeya
Electronic Theses and Dissertations
Variable selection is one of the standard ways of selecting models in large scale datasets. It has applications in many fields of research study, especially in large multi-center clinical trials. One of the prominent methods in variable selection is the penalized likelihood, which is both consistent and efficient. However, the penalized selection is significantly challenging under the influence of random (frailty) covariates. It is even more complicated when there is involvement of censoring as it may not have a closed-form solution for the marginal log-likelihood. Therefore, we applied the penalized quasi-likelihood (PQL) approach that approximates the solution for such a …